Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review - Report - MDSpire
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Artificial intelligence for prediction of clinical response and therapeutic value in interventional pain management: a scoping review
Clinical Report: Utilizing Artificial Intelligence in Interventional Pain Management
Overview
This scoping review maps the application of artificial intelligence (AI) in predicting clinical outcomes and treatment efficacy in interventional pain management, based on 25 studies. It highlights the variability in clinical responses and the need for robust external validation of AI models in this field.
Background
Chronic pain is a prevalent health issue that significantly impacts quality of life and healthcare systems, as evidenced by recent studies. Interventional pain management has become essential for patients with pain unresponsive to conservative treatments, with various procedures being utilized. However, variability in treatment outcomes necessitates innovative approaches, such as AI, to improve decision-making and predict therapeutic value.
Data Highlights
The review included 25 studies focusing on predictive applications of AI in various interventional pain procedures, including epidural injections and spinal cord stimulation, with specific methodologies and outcomes detailed in the studies.
Key Findings
AI models were primarily used to explore clinical response patterns, durability of benefit, and procedural risks, as reported in the included studies.
Most studies were retrospective and relied on internal validation, with limited external validation.
Outcome domains included opioid use trajectories and functional recovery.
Methodological heterogeneity was noted across the included studies.
Further prospective studies with robust external validation are necessary for clinical implementation.
Clinical Implications
The findings indicate that while AI has potential in predicting outcomes in interventional pain management, the current evidence is limited by methodological issues.
Conclusion
AI has been applied across various interventional pain domains, but limitations in study design and validation hinder its clinical applicability. Further research is essential to enhance the reliability of AI in this field.
